Whisper Transcribe
PYPI · WHISPER-TRANSCRIBE-MCP · SCANNED SEP 20
Transcribe audio locally with faster-whisper or through the OpenAI Whisper API.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
- 1 of 15 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency32
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- License check failed: the license (MIT License) isn't a recognized OSI-approved license. See how to fix → Fail
- Actively maintained (last published 58 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability76
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 378 tokens (~126/item across 3 items; 3 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management87
- Stability observed for 26 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 3 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 3 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
How do I install the Whisper Transcribe MCP server?
Whisper Transcribe runs locally as a PyPI package, launched with uvx whisper-transcribe-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · whisper-transcribe-mcp
claude mcp add zahirinatzuke-whisper-transcribe-mcp -- uvx whisper-transcribe-mcp
{
"mcpServers": {
"zahirinatzuke-whisper-transcribe-mcp": {
"command": "uvx",
"args": [
"whisper-transcribe-mcp"
]
}
}
} {
"servers": {
"zahirinatzuke-whisper-transcribe-mcp": {
"command": "uvx",
"args": [
"whisper-transcribe-mcp"
]
}
}
} codex mcp add zahirinatzuke-whisper-transcribe-mcp -- uvx whisper-transcribe-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"zahirinatzuke-whisper-transcribe-mcp": {
"type": "local",
"command": [
"uvx",
"whisper-transcribe-mcp"
],
"enabled": true
}
}
} openclaw mcp add zahirinatzuke-whisper-transcribe-mcp --command uvx --arg whisper-transcribe-mcp
mcp_servers:
zahirinatzuke-whisper-transcribe-mcp:
command: "uvx"
args: ["whisper-transcribe-mcp"] {
"McpServers": {
"zahirinatzuke-whisper-transcribe-mcp": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"whisper-transcribe-mcp"
]
}
}
} assistant mcp add zahirinatzuke-whisper-transcribe-mcp -t stdio -c uvx -a whisper-transcribe-mcp
{
"mcpServers": {
"zahirinatzuke-whisper-transcribe-mcp": {
"command": "uvx",
"args": [
"whisper-transcribe-mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 19 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 80 to 83. That category is still filling its 30-day observation window: 24 days of observed history at the previous scan, 25 at this one. The score rises as the window fills, whether or not the server changes.
- 18 Sept 26 +12
- Malware scan: unverified → pass ▲ security
- Stability: pass → 0.80 functional
- 17 Sept 26 0
- Stability: 0.97 → pass security
- 16 Sept 26 −14
- Malware scan: pass → unverified ▼ security
- 14 Sept 26 +16
- Malware scan: unverified → pass ▲ security
- 12 Sept 26 −2
- Stability: pass → 0.83 functional
- 11 Sept 26 0
- Stability: 0.97 → pass security
- 10 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 93 to 97. That category is still filling its 30-day observation window: 28 days of observed history at the previous scan, 29 at this one. The score rises as the window fills, whether or not the server changes.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 20 Sept 2026 · Analysed pypi/whisper-transcribe-mcp@1.1.2
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 15 packages
| Packages resolved | 15 |
|---|---|
| Stale | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
list_models List Models ~17
List available Whisper model sizes and current configuration.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
transcribe_base64 Transcribe Base64 ~157
Transcribe audio provided as a base64-encoded string.
| Name | Type | Req | Description |
|---|---|---|---|
| audio_base64 | string | yes | Base64-encoded audio data. |
| extension | string | – | File extension for the temp file (mp3, wav, m4a, ogg, etc.). |
| language | – | – | Language code. Auto-detected if not provided. |
| model_size | – | – | Local model size. Ignored when using the OpenAI backend. |
| post_process | boolean | – | If True, passes the transcription through GPT to fix spelling, grammar, and punctuation. Requires the openai package. |
| post_process_prompt | – | – | Custom system prompt for post-processing. Use this to provide domain-specific context, proper nouns, or product names. |
Structured output declared, but exposes no named fields.
No examples provided.
transcribe_file Transcribe File ~204
Transcribe an audio file to text.
| Name | Type | Req | Description |
|---|---|---|---|
| file_path | string | yes | Absolute path to the audio file (mp3, wav, m4a, ogg, flac, etc.) |
| language | – | – | Language code (e.g. 'es', 'en', 'fr'). Auto-detected if not provided. |
| model_size | – | – | Local model size: tiny, base, small, medium, large-v3. Ignored when using the OpenAI backend. Defaults to the WHISPER_MODEL environment variable (default: 'base'). |
| post_process | boolean | – | If True, passes the transcription through GPT to fix spelling, grammar, and punctuation. Requires the openai package. |
| post_process_prompt | – | – | Custom system prompt for post-processing. Use this to provide domain-specific context, proper nouns, or product names that Whisper may have misspelled. Falls back to… |
Structured output declared, but exposes no named fields.
No examples provided.
What is the Whisper Transcribe MCP server?
Whisper Transcribe is an MCP server listed in the public MCP registry as io.github.ZahiriNatZuke/whisper-transcribe-mcp. Transcribe audio locally with faster-whisper or through the OpenAI Whisper API. This page covers its PyPI package (whisper-transcribe-mcp).
Is the Whisper Transcribe MCP server safe to use?
Whisper Transcribe scores 80 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 20 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Whisper Transcribe MCP server expose?
Whisper Transcribe exposes 3 tools: transcribe_file, transcribe_base64, list_models. Their descriptions and schemas cost roughly 378 tokens of context every time the server is loaded.
Is the Whisper Transcribe MCP server still maintained?
Whisper Transcribe is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.